Here is an article by one of our Top 5 Budding Data Scientists, Bhaskar Bharat. Read how he envisions India to become a leader in the AI space. Bhaskar also ranked among INSAID’s Top 5 Budding Data Scientists, click here to know more!
Artificial intelligence (AI) has finally caught the Indian government’s attention.
In February 2018, delivering his budget speech, the then finance minister, Arun Jaitley told parliament that the government think-tank- NITI Aayog, will spearhead a National Program on AI, including research and development. The intent showed in the numbers: Budget allocation for Digital India, the government’s umbrella initiative to promote AI, machine learning, 3D printing, and other technologies, was almost doubled to Rs 3,073 crore ($477 million).
Soon after that in June 2018, NITI Aayog released a discussion paper titled ”National Strategy for Artificial Intelligence” articulating what India wants to be in an AI-centric future. This paper pitches India as the AI “garage for the emerging and developing economies”. The focus will be on five key sectors— healthcare, agriculture, education, smart cities & infrastructure and smart mobility & transportation.
Pre-requisites for AI Ecosystem
India needs to focus their attention on the 5 primary pre-requisites that are essential for an AI ecosystem to flourish in a country.
AI Research — AI is essentially an evolving field and many of the AI technologies are maturing and getting better off with research, evolution happening in terms of the better approach to tackle some of its technical limitations.
So, an ecosystem embracing AI research is very essential for a country to progress in the field of AI whether such researches to be carried out in academia, within R&D cells of AI companies or in collaboration with the government.
Usable Data — It is true that organisations have access to more data today than ever before. However, data-sets that are relevant for AI applications to learn are indeed rare. The most powerful AI machines are the ones that are trained on supervised learning. This training requires labelled data – data that is organised to make it ingestible for machines to learn.
Labelled data is limited. In the not-so-distant future, the automated creation of increasingly complex algorithms, largely driven by deep learning, will only aggravate the problem.
There’s a ray of hope though. As a trend that’s fast catching up, organisations are investing in design methodologies, trying to figure out how to make AI models learn despite the scarcity of labelled data. ‘Transfer Learning’, ‘Unsupervised/Semi-Supervised Learning’, ‘Active Learning’, and so on are just a few examples of the next-generation AI algorithms that can help resolve this.
‘Digital India’ campaign is the first step taken by Indian Government in this direction to ensure that data in digital form is made available for the next level of processing. The Aadhar or UIDAI Project in India is the largest ever unique identifier project in the world. India is actively pushing more and more into creating public data-sets.
Wealth of AI Engineers and Data Scientists — Traditional software engineers to become AI affluent needs special skills in the areas of machine learning, deep learning, NLP etc. AI researchers coming from data science background need even higher skills and background to carryout research on different areas of AI. Adequate pool of such AI engineers and AI researchers are essential for AI to grow in a country. The more your Tech colleges and universities producing these skilled resources the more your chances to be the leader in the race.
Support from Government and awareness among citizens– Backing from the Government and their support; may it be building infrastructure for AI, framing policies that supports AI to grow, encouraging enterprises in adoption of AI or whatever other form it could be; the government of a country plays a major role democratizing a rising technology like AI. Allocation of almost doubled budget for this cause and NITI Aayog’s ‘AIForAll’ strategy clearly show the Government’s intention and seriousness in this direction.
Funding and VC Ecosystem– Proper funding and a mature Venture Capitalist ecosystem is vital for AI start-ups and other AI initiatives in a country to grow. Sometimes it’s not just funding the AI start-ups but essentially guiding them through their journey, believing in and being part of their vision is very important which can only be expected from a mature VC ecosystem. Despite having all other AI ingredients most of the developing countries like India and its young entrepreneurs and start-ups are essentially struggling in this particular space.
Key Accelerators in India’s Growth Story
Some of the advantages India has over other countries in terms of AI are:
Strong IT Services and Existing IT Ecosystem
The key advantage for India would be the Information Technology (IT) and Information Technology enabled Services (ITeS) sectors which will make it easy for Indian tech providers to transition into AI services, given that well-developed ecosystems have evolved over the past 25 years in cities like Bangalore and Hyderabad. Bangalore has always been seen as the Silicon Valley of India and today there are lots of analytics companies here. It has all the ingredients to be a leader in the AI space. The state government is interested in planning and grooming for start-ups in this space as witnessed by the launch of the Centre for Excellence (CoE) in AI setup by the GOI and NASSCOM in Bangalore.
Diversity (at Scale) May be a Massive Opportunity
In India, people speak over 40+ formal languages in about 800+ dialects. There are 22 national languages and if you want to build a neural network for speech, India is the best place to build that neural net. If you can build for India, you can most likely build it for other parts of the world. In this respect, India – with all of its language challenges – could be a petri dish for translation-oriented AI applications. The market for this technology – especially when backed by the Indian government – may well rival the kind of AI innovations developed around translation in other parts of the world.
Hundreds of Millions of People Coming Online
According to a report by the Telecom Regulatory Authority of India (TRAI) the total number of internet subscribers in the country as a percentage of the overall population increased by 12.01% from December 2013 to reach 267.39 million in December 2014. In the public sector, we have an advantage of scale, the amount of data that can potentially be gathered is huge. For example, leveraging data to provide access to services is a huge differentiator in the healthcare sector for applications like disease prevention or nutrition. The direct switch to mobile platforms in India means that large-scale AI projects in India can be somewhat insulated from issues cropping up from legacy systems. This might also lead to a greater immediate mobile-fluency for India’s start-up and developer communities, who need to appeal to an almost exclusively mobile market. In the future, we can expect that AI software will also potentially have this advantage in India as compared to developed countries where the ratio is more evenly distributed among mobile and fixed wireless users.
With half of the country’s population below the age of 25, a pertinent step would be to prepare the young workforce by exposing them to the tech-enabled future of work with AI interfaces, machine learning, and increased automation.
Online training programs, inclusion of AI and automation in the existing education curriculum, and corporate training programs for new hires can achieve this without much structural change and investment. For this, the political leadership also needs a better understanding of automation technologies and their implications for the Indian economy.
Top Challenges in India’s Growth Story!
While there is a lot of enthusiasm around the AI growth trajectory in India with numerous developments in the field of AI, ML and robotics, there are still a lot of challenges to be tackled. Even though Indian start-up founders are seeing institutional support from the Indian government, there are still a lot of implementation issues to overcome.
Overcoming The Cost Issues For Implementing AI-based Solutions: AI-based solutions and products are costly to train. The technology lacks maturity and is yet to give a solid return on investment in many sectors. Besides, large companies are still grappling with the culture change that is required to adopt AI.
Lack Of Data Conundrum: Given that data is the new oil, big tech giants are evolving into data oligarchs, many of them even buying out companies for their data. However, existing data sets in India, whether for labor markets or health systems, are fragmented, unrepresentative or outdated. Further, there are large digital divides, for example between urban and rural areas and between men and women. Less than 30% of India’s internet users are women and only 14% of women in rural India own a mobile phone. Algorithms based on existing data sets will thus undoubtedly have a distorted picture of social reality, blind to the behaviors, needs and experiences of numerous social groups. The digitization of government, financial and other services, could partially address this issue.
But for most people, access to, and participation in, existing systems, whether for healthcare, social protection or employment, is through informal, unregistered and unaccounted channels and systems. Unless these systems themselves transform or new means and metrics are found for capturing data, the challenge will remain. Getting more users online or digitizing access to public service will not create usable data sets by itself – this will require existing socioeconomic systems to transform and deeply embedded behavioral patterns and social practices to shift.
AI solutions will disrupt labor markets: The deployment of AI solutions in industry will disrupt labor markets in India, to the detriment of a bulk of the labor force. This will make it increasingly difficult for India to generate employment through an export-oriented manufacturing strategy. This poses a particular challenge for India, given that a large part of its population is low-skilled, and thus traditionally best absorbed within large-scale manufacturing industries. Further, it is unlikely new job creation can offset such losses.
The people who lose their jobs are unlikely to be the same ones to take up newly created jobs – a middle-aged low-skill worker will find it very difficult to re-skill or up-skill fast enough. The newly created high-skilled jobs are likely to be significantly fewer in number and unable to absorb India’s large labour surplus.
Lack of Collaboration Between Industry and Academia: The two underlying factors here are larger salaries lie in the corporate sector, which is potentially creating a dearth of mentors for the next generation of software developers looking to transition into AI and the availability data. Academia and Industry collaboration is a serious issue in India. Although we have a lot of universities, the incentives are skewed towards the corporate sector. People who have an understanding of the technology may not be inclined to teach the next generation at universities, since working at the larger companies is far more lucrative today.
Much of the “AI upskilling” of India’s development talent will occur on the job in the cutting-edge work environments of venture-backed companies, as opposed to in the classroom. Talent will be the biggest strength for India with respect to AI. But AI is still new, so current talent in the market is very limited. Industry-university partnerships where students can work with real world data science applications and reskilling of existing work-forces is one area where government definitely have to look into.
Take that AI enabled leap of faith
India is a developing nation where we are still struggling with poverty and providing food and shelter and fulfilling basic needs of being a human being. But, in prioritizing food and shelter, we haven’t put our dreams on hold. India is behind countries like China and the US from an academic research perspective, and hence behind translating that knowledge to start- ups, and products. But given that we have probably the second largest number of IT professionals in the world, and we’re good at retraining these people, we’re going to catch up soon. The Government too had a favourable attitude towards the use of AI to meet these goals. AI is expected to create new areas of economic opportunity and wealth creation. We certainly hope that India can make the most of artificial intelligence – both for the wellbeing of its citizens and to ensure the health of its economy. The next five years will be a time to set both the pace and trend of AI adoption in the country.